Data and AI Engineer

About the role

The Data and AI Engineer will join Yumbah’s technology team to design, build and operate production‑grade data models, pipelines and AI solutions that connect finance, aquaculture and commercial operations. You will translate the company’s data and AI strategy into reliable systems that enable smarter decision‑making across the business.

Key responsibilities

• Build and maintain the core data engineering environment, including pipelines, lakehouses/warehouses and BI reporting assets.
• Design and implement a canonical data model that integrates financial, operational and farming data.
• Develop and deploy a secure conversational AI agent that provides natural‑language access to enterprise data with governance controls.
• Implement semantic modelling and self‑service BI capabilities in Power BI, including DAX and row‑level security.
• Automate financial reporting, reconciliation and control processes to improve accuracy and reduce manual effort.
• Establish production‑grade standards for AI and data tools, covering monitoring, auditability and prototype‑to‑production pathways.
• Support integration of IoT and operational data sources from farming sites into the enterprise data platform.
• Ensure data governance, security, privacy and compliance are embedded in all solutions.

Required profile

• Hands‑on experience in data engineering or analytics engineering roles.
• Strong proficiency in Python and SQL with a track record of delivering production‑quality code.
• Experience with modern cloud data platforms, preferably Microsoft Fabric.
• Advanced Power BI skills, including data modelling, DAX, semantic layers and row‑level security.
• Familiarity with LLM‑based systems such as retrieval‑augmented generation, agentic workflows or tool‑calling architectures.
• Practical experience using AI development tools in a production‑aware environment.
• Ability to translate complex business problems into scalable data and AI solutions.
• Excellent communication skills for collaboration with both technical and non‑technical stakeholders.

Required skills

• Python
• SQL
• Microsoft Fabric
• Power BI (DAX, semantic layers, RLS)
• Large Language Model (LLM) techniques (RAG, agentic workflows, tool‑calling)
• AI development tools
• Data modelling
• Data pipelines
• Analytics engineering
• IoT data integration